We explore it with an empirical study shown in Figure REF . Suppose that we train a local model centered on a particular anchor user, considering the top-1% of its closest neighbors. When we infer the basis of the same neighborhood as in previous works [1]}, [2]}, [3]}, the accuracy of the local model (the diamond point at the bottom left) is much worse than that of the global model, implying that the training data is insufficient to learn the local model. Then, maintaining the same neighborhood in the inference step, we expand the neighborhood in the training step to 5%, 10%, 20%, and so on. We observe that the accuracy of the local model improves as the size of the training data increases up to 20%, even if the augmented users are less similar to the anchor user. From this pilot study, it is clear that the number of neighbors in the training step tends to appreciate at the cost of reduced similarity, whereas the inference step maintains a small number of neighbors for the small and coherent sub-community.
